



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''decision_transformer'''
     UpperCamelCase__		     =					['''past_key_values''']
     UpperCamelCase__		     =					{
         '''max_position_embeddings''': '''n_positions''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }




     def __init__(					self			,    a__=1_7			,    a__=4			,    a__=1_2_8			,    a__=4_0_9_6			,    a__=True			,    a__=1			,    a__=1_0_2_4			,    a__=3			,    a__=1			,    a__=None			,    a__="relu"			,    a__=0.1			,    a__=0.1			,    a__=0.1			,    a__=1e-5			,    a__=0.0_2			,    a__=True			,    a__=True			,    a__=5_0_2_5_6			,    a__=5_0_2_5_6			,    a__=False			,    a__=False			,    **a__			,    ):
         A__					=       state_dim
         A__					=       act_dim
         A__					=       hidden_size
         A__					=       max_ep_len
         A__					=       action_tanh
         A__					=       vocab_size
         A__					=       n_positions
         A__					=       n_layer
         A__					=       n_head
         A__					=       n_inner
         A__					=       activation_function
         A__					=       resid_pdrop
         A__					=       embd_pdrop
         A__					=       attn_pdrop
         A__					=       layer_norm_epsilon
         A__					=       initializer_range
         A__					=       scale_attn_weights
         A__					=       use_cache
         A__					=       scale_attn_by_inverse_layer_idx
         A__					=       reorder_and_upcast_attn

         A__					=       bos_token_id
         A__					=       eos_token_id

         super().__init__(bos_token_id=a__			,    eos_token_id=a__			,    **a__)






'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   field(default="""audio-classification"""							,				metadata={"""include_in_asdict_even_if_is_default""": True}			)
   _a    						=   Features({"""audio""": Audio()}			)
   _a    						=   Features({"""labels""": ClassLabel}			)
   _a    						=   "audio"
   _a    						=   "labels"







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if self.label_column not in features:
       raise ValueError(f"Column {self.label_column} is not present in features."   )
     if not isinstance(features[self.label_column]   ,				UpperCamelCase   ):
       raise ValueError(f"Column {self.label_column} is not a ClassLabel."   )
     __a								=       copy.deepcopy(self   )
     __a								=       self.label_schema.copy()
     __a								=       features[self.label_column]
     __a								=       label_schema
     return task_template







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict[str, str]:
     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }



